A modified deep learning architecture for vehicle detection in traffic monitoring system. Issue 9 (21st October 2021)

• Record Type: Journal Article
• Authors: Haritha, H.; Thangavel, Senthil Kumar
• Is Part Of: International journal of computers and applications. Volume 43:Issue 9(2021)
• Page Start: 968
• Page End: 977
• Publication Date: 2021-10-21
• Subjects: Convolutional neural network -- vehicle detection -- scalability -- global average pooling -- ROI pooling; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2019.1662171
• Languages: English
• ISSNs: 1206-212X

Abstract

Abstract : Deployment of adequate surveillance and security measures is crucial in today's scenario. The proposed system is designed to handle the surveillance that detects the vehicles present in the traffic. With the evolution of Deep convolutional neural network, the vision-based vehicle detection has reached a higher level in performance. The proposed method deals with the detection of vehicles and capable of handling the distant small-scale vehicles without bothering the image scalability. The method uses Deep Learning along with ROI pooling for handling image scalability. The method consists of a noise-reducing component and the convolutional layers. The method calculates varying scales of the input and does the filtering according to each scale without generalizing. Thereby it handles the image scalability issue. The filter size is small in the proposed method, hence there is a reduction in system complexity and an increase in performance rate. The global average pooling is introduced to the final layers so that the overfitting is avoided. The performance of the system is assessed by the comparison with already established methods for vehicle detection like MS-CNN and YOLO v2 and provides much better experimental results.